OPTIMALISASI PREDIKSI JARAK TEMPUH KENDARAAN LISTRIK MENGGUNAKAN XGBOOST DAN FEATURE SELECTION RANDOM FOREST
DOI:
https://doi.org/10.33480/zahvvq98Kata Kunci:
Electric Vehicle (EV), Machine Learning, Prediksi, Random Forest, XGBoostAbstrak
Electric vehicle (EV) adoption is increasing with increasing awareness of clean energy and environmental concerns. However, range anxiety, the uncertainty in estimating driving range, remains a major barrier. This study aims to develop a predictive model for EV range based on technical specifications to provide more accurate estimates and alleviate user concerns. A Machine Learning approach is applied, using Random Forest for feature selection and XGBoost as the primary prediction algorithm. The dataset consists of 478 EV records with 22 attributes, including battery capacity, efficiency, dimensions, and speed. Key features affecting range prediction include battery_capacity_kWh, efficiency_wh_per_km, and height_mm. The XGBoost model demonstrates strong predictive performance with an R² of 0.978, an MAE of 10.555, and an RMSE of 15.180. These results suggest that combining Random Forest and XGBoost offers a promising solution to improve the accuracy of EV range estimation, potentially reducing range anxiety and supporting wider EV adoption
Unduhan
Referensi
Abuqila, M., Nassar, Y., & Nyasapoh, M. (2025). Estimation of the Storage Capacity of Electric Vehicle Batteries under Real Weather and Drive-mode Conditions: A Case Study. Wadi Alshatti University Journal of Pure and Applied Sciences, 58–71.
Al-Ghaili, A. M., Kasim, H., Aris, H., & Al-Hada, N. M. (2022). Can electric vehicles be an alternative for traditional fossil-fuel cars with the help of renewable energy sources towards energy sustainability achievement? Energy Informatics, 5(Suppl 4), 60.
Al-Wreikat, Y., Serrano, C., & Sodré, J. R. (2021). Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving. Applied Energy, 297, 117096.
Chakraborty, P., Parker, R., Hoque, T., Cruz, J., Du, L., Wang, S., & Bhunia, S. (2022). Addressing the range anxiety of battery electric vehicles with charging en route. Scientific Reports, 12(1), 5588.
Dudic, B. (2024). Global Electric Car Market. International Conference “New Technologies, Development and Applications,” 158–164.
Elkabalawy, M., Al-Sakkaf, A., Mohammed Abdelkader, E., & Alfalah, G. (2024). CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors. Sustainability, 16(17), 7249.
Elshewey, A. M., Osman, A. M., Youssef, R. Y., El-Bakry, H. M., & Hassan, S. A. Z. (2025). Enhancing forest fires classification using a hybrid convolutional and BiLSTM deep learning model. Modeling Earth Systems and Environment, 11(5), 379.
Fatima, S., Hussain, A., Amir, S. Bin, Ahmed, S. H., & Aslam, S. M. H. (2023). XGBoost and random forest algorithms: an in depth analysis. Pakistan Journal of Scientific Research, 3(1), 26–31.
Feng, J., Yao, Y., Liu, Zhenfeng, & Liu, Zhenling. (2024). Electric vehicle charging stations’ installing strategies: Considering government subsidies. Applied Energy, 370, 123552.
Gu, M., Kang, S., Xu, Z., Lin, L., & Zhang, Z. (2025). AE-XGBoost: A Novel Approach for Machine Tool Machining Size Prediction Combining XGBoost, AE and SHAP. Mathematics, 13(5), 835.
Hasib, S. A., Saha, D. K., Islam, S., Tanvir, M., & Alam, M. S. (2021). Driving range prediction of electric vehicles: A machine learning approach. 2021 5th International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), 1–6.
Hossain, M. R. (2025). Predicting customer churn in telecommunications with machine learning models. Asian Journal of Research in Computer Science, 18(1), 53–66.
Hussain, I., Ching, K. B., Uttraphan, C., Tay, K. G., & Noor, A. (2025). Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study. Scientific Reports, 15(1), 16124.
Mondal, S., Ramesh, H. N., Ma, X., Cheng, S., Angeles, T., & Sun, S. (2024). Reducing electric vehicle range anxiety with machine learning models incorporating human behavior.
Naseri, H., Waygood, E. O. D., Wang, B., & Patterson, Z. (2023). Interpretable machine learning approach to predicting electric vehicle buying decisions. Transportation Research Record, 2677(12), 704–717.
Park, J.-H., & Baek, S. H. (2024). Two-Step Methodology for Statistical Anomaly Detection and Prediction Using XGBoost Regression in Blower Motor Vibration Time Series Data. International Journal of Industrial Engineering: Theory, Applications and Practice, 31(4).
Powell, B., & Johnson, C. (2024). Impact of Electric Vehicle Charging Station Reliability, Resilience, and Location on Electric Vehicle Adoption. National Renewable Energy Laboratory (NREL), Golden, CO (United States).
Rainieri, G., Buizza, C., & Ghilardi, A. (2023). The psychological, human factors and socio-technical contribution: A systematic review towards range anxiety of battery electric vehicles’ drivers. Transportation Research Part F: Traffic Psychology and Behaviour, 99, 52–70.
Saltz, J. S. (2021). CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps. 2021 IEEE International Conference on Big Data (Big Data), 2337–2344. https://doi.org/10.1109/BigData52589.2021.9671634
Sang, V. T. D., Duong, Q. H., Zhou, L., & Arranz, C. F. A. (2024). Electric vehicle battery technologies and capacity prediction: a comprehensive literature review of trends and influencing factors. Batteries, 10(12), 451.
Schröer, C., Kruse, F., & Gómez, J. M. (2021a). A Systematic Literature Review on Applying CRISP-DM Process Model. Procedia Computer Science, 181, 526–534. https://doi.org/https://doi.org/10.1016/j.procs.2021.01.199
Schröer, C., Kruse, F., & Gómez, J. M. (2021b). A systematic literature review on applying CRISP-DM process model. Procedia Computer Science, 181, 526–534. https://doi.org/10.1016/j.procs.2021.01.199
Shrestha, S., Baral, B., Shah, M., Chitrakar, S., & Shrestha, B. P. (2022). Measures to resolve range anxiety in electric vehicle users. International Journal of Low-Carbon Technologies, 17, 1186–1206.
Sinha, P., Paul, K., Deb, S., & Sachan, S. (2023). Comprehensive review based on the impact of integrating electric vehicle and renewable energy sources to the grid. Energies, 16(6), 2924.
Sree, K. S., Karthik, J., Niharika, C., Srinivas, P., Ravinder, N., & Prasad, C. (2021). Optimized conversion of categorical and numerical features in machine learning models. 2021 Fifth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), 294–299.
Studer, S., Bui, T. B., Drescher, C., Hanuschkin, A., Winkler, L., Peters, S., & Müller, K.-R. (2021). Towards CRISP-ML (Q): a machine learning process model with quality assurance methodology. Machine Learning and Knowledge Extraction, 3(2), 392–413.
Sudjoko, C., Sasongko, N. A., Utami, I., & Maghfuri, A. (2021). Utilization of electric vehicles as an energy alternative to reduce carbon emissions. IOP Conference Series: Earth and Environmental Science, 926(1), 012094.
Urvish Ahir. (2025). Electric Vehicle Specs Dataset (2025). Kaggle. https://www.kaggle.com/datasets/urvishahir/electric-vehicle-specifications-dataset-2025
Yaghoubi, Elaheh, Yaghoubi, Elnaz, Khamees, A., Razmi, D., & Lu, T. (2024). A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior. Engineering Applications of Artificial Intelligence, 135, 108789.
Yang, D., Liu, H., Li, M., & Xu, H. (2023). Data-driven analysis of battery electric vehicle energy consumption under real-world temperature conditions. Journal of Energy Storage, 72, 108590.
Zhang, W., Fang, X., & Sun, C. (2023). The alternative path for fossil oil: electric vehicles or hydrogen fuel cell vehicles? Journal of Environmental Management, 341, 118019.
Zhang, X., Zhang, Z., Liu, Y., Xu, Z., & Qu, X. (2024a). A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation. Renewable Energy, 234, 121243.
Zhang, X., Zhang, Z., Liu, Y., Xu, Z., & Qu, X. (2024b). A review of machine learning approaches for electric vehicle energy consumption modelling in urban transportation. Renewable Energy, 234, 121243.
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 M. Rangga Ramadhan Saelan, Riyan Latifahul Hasanah, Siti Fauziah
Artikel ini berlisensi Creative Commons Attribution-NonCommercial 4.0 International License.
Penulis yang menerbitkan jurnal ini menyetujui ketentuan berikut:
1. Penulis memegang hak cipta dan memberikan hak jurnal mengenai publikasi pertama dengan karya yang dilisensikan secara bersamaan di bawah Creative Commons Attribution 4.0 International License. yang memungkinkan orang lain untuk berbagi karya dengan pengakuan atas karya penulis dan publikasi awal pada jurnal.
2. Penulis dapat memasukkan pengaturan kontrak tambahan yang terpisah untuk distribusi non-eksklusif dari versi jurnal yang diterbitkan (misalnya, mengirimkannya ke repositori institusional atau menerbitkannya dalam sebuah buku), dengan pengakuan atas publikasi awalnya pada Jurnal.
3. Penulis diizinkan dan didorong untuk memposting karya mereka secara online (misalnya, dalam penyimpanan institusional atau di situs web mereka) sebelum dan selama proses pengiriman, karena hal itu dapat menghasilkan pertukaran yang produktif, serta kutipan dari karya yang diterbitkan sebelumnya.
